Automated Word Prediction in Bangla Language Using Stochastic Language Models

TL;DR

This paper explores the use of stochastic N-gram language models for automated word prediction in Bangla, aiming to improve typing efficiency and accuracy, especially for users with disabilities.

Contribution

It introduces the application of various N-gram models to Bangla word prediction, providing a baseline for automated Bangla typing systems.

Findings

High prediction accuracy achieved with N-gram models

Effective auto-completion reduces keystrokes and misspellings

Promising results suggest potential for real-world application

Abstract

Word completion and word prediction are two important phenomena in typing that benefit users who type using keyboard or other similar devices. They can have profound impact on the typing of disable people. Our work is based on word prediction on Bangla sentence by using stochastic, i.e. N-gram language model such as unigram, bigram, trigram, deleted Interpolation and backoff models for auto completing a sentence by predicting a correct word in a sentence which saves time and keystrokes of typing and also reduces misspelling. We use large data corpus of Bangla language of different word types to predict correct word with the accuracy as much as possible. We have found promising results. We hope that our work will impact on the baseline for automated Bangla typing.